Representations are based on a set of known data, referred to as a dictionary. Sparse representation is achieved by selecting the items from the dictionary that are closest to the content to be compressed, and only transmitting information related to these items.
Generally, an algorithm of the “matching pursuit” family, (“MP algorithm”) is implemented in order to determine the sparse representation based on the content to be compressed and the dictionary.
Computer tools implementing this type of scheme are very powerful, as the degree of compaction of the approximation obtained is high, and the matching pursuit algorithms are highly effective. However, it is difficult to obtain a dictionary that performs on data of these varied types, as this necessitates a large dictionary, which entails increased storage and calculation costs.
Various methods have been developed in order to compensate this disadvantage. One of these methods is the “K-SVD” method. According to this method, a dictionary is optimised based on the number of iterations intended.
Although this is satisfactory to a certain extent, this method generally remains insufficient. In fact, it provides a dictionary that is optimised on average, but not necessarily for each of the iterations of the MP algorithm. Furthermore, this dictionary is fixed for all iterations, and thus unsuitable if one decides to change the number of iterations.